Trust calibration through perceptual and predictive information of the external context in autonomous vehicle

背景(考古学) 感知 校准 预测分析 毒物控制 计算机科学 人为因素与人体工程学 心理学 计算机安全 工程类 医疗急救 数据科学 医学 地理 数学 统计 考古 神经科学
作者
Qi Gao,Lehan Chen,Yanwei Shi,Yuxuan Luo,Mowei Shen,Zaifeng Gao
出处
期刊:Transportation Research Part F-traffic Psychology and Behaviour [Elsevier BV]
卷期号:107: 537-548 被引量:7
标识
DOI:10.1016/j.trf.2024.09.019
摘要

• Enhancing drivers’ perception and prediction of external context aids trust calibration in L3 driving. • Predictive information marked safe/dangerous zones boosts overall trust and avoid over-trust. • Number of accidents remains unaffected by SA improvements. Maintaining an appropriate level of trust is critical for driving safety in autonomous vehicles. While enhancing the driver’s situation awareness (SA) of system information in autonomous driving is known to significantly promote trust calibration, it remains unclear whether enhancing the driver’s SA of the external context during driving contributes to this calibration. This study addresses this gap by improving SA of the external context during Level 3 (L3) driving automation across various driving environments. Driving contexts were manipulated using distinct road conditions containing low, medium, or high contextual risks. To enhance driver’s SA of the driving context, we redesigned the in-vehicle central control panel to display real-time perceptual and predictive information about the external driving context. We hypothesized that SA of driving contexts would facilitate trust calibration rather than merely enhancing trust, allowing trust to adjust to appropriate levels under different driving conditions. Experiment 1 examined the impact of perceptual information about the road, traffic infrastructure, and surrounding vehicles on drivers’ trust. We found that driver’s trust decreased with increased contextual risk only when the reconfigured panel was used, while the number of accidents was not affected. Experiment 2 investigated the effect of predictive information about the external context on drivers’ trust by marking safe and dangerous zones around driver’s vehicle with green and red areas, respectively. We revealed that the predictive information calibrated the trust according to road conditions and increased overall trust levels, while the number of accidents was not affected. Together, these findings suggest that enhancing perception and prediction of external contexts helps drivers align their trust with contextual risk levels in L3 driving automation without compromising driving safety.
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